Case Classification System Based on Taiwanese Civil Summary Court Cases
摘要
This experiment classifies cases based on the top 20 most frequently used categories of case reasons found in civil summary court judgments provided by the Judicial Yuan of Taiwan from 2012 to 2022. We built case classifiers using two methods: machine learning with TF-IDF+SVM and deep learning with BERT. We then compared the results of both classifiers. In the classification results using TF-IDF+SVM, an accuracy of 89.3% was achieved, while with BERT, an accuracy of 93.825% was achieved.